This section displays how much of each land use type is covered by trees. For example, a value of 60 for ‘Streets’ means that 60% of total street area is under canopy coverage. Accordingly, if ‘Parks’ is a low number, it means parks are not shaded, not that there are few parks in a region, it is just likely they are open spaces.
Code
street_trees_grouped <-coverage(exp =expression(zone_short =='roads'), type ='street', group = grouping)reserve_trees_grouped <-coverage(exp =expression(feature_type =='reserve'), type ='reserve', group = grouping) resi_trees_grouped <-coverage(exp =expression(zoning_permits_housing =='Housing permitted'), type ='residential', group = grouping) #combine into one and cleancombined_df <- street_trees_grouped %>%left_join(reserve_trees_grouped, by = grouping) %>%left_join(resi_trees_grouped, by = grouping) %>% dplyr::select(c(grouping,'street_percentage', 'reserve_percentage', 'residential_percentage')) %>%left_join(grouping_sf %>%st_drop_geometry() %>%select(grouping, distance), by = grouping) %>%rowwise() %>%# mutate(distance = distance_map[as.character(as.name(grouping))]) %>%mutate(distance = distance /1000) %>%mutate(across(where(is.numeric), ~round(.x, 1))) %>%mutate(across(ends_with('percentage'), ~ifelse(is.na(.x), 0, .x) )) %>%ungroup() %>%as.data.frame()sd_combined = SharedData$new((combined_df))
Note: the table below is sorted by street tree coverage by default. Click other column names to sort them.
Note: There might be innaccuracies in the street coverage data when using Suburbs as grouping unit, as not all Statistical Area 1s fit uniquely within a suburb. As such, SA1s are assigned into suburbs based on the one they have the highest overlap with.
Code
filter_slider("distance", "Distance to CBD", sd_combined, ~distance, min =0, max =max(sd_combined$data()$distance), width ='75%')
YM recently released Housing Targets, a data-driven model which allocates housing targets by council.
This page calculates how much tree cover might be lost in the upzoning of residential lots, and then calculating how to offset this loss by increasing coverage of street trees
This section breaks down canopy coverage by zone type. As expected, low density residential and civic land (parks) top the list. It is tempting to note that Greenfield is below industrial, however, Greenfield by definition has not had enough time for planted trees to grow large enough for canopy cover.
Note: in the scatter plots below, suburbs with less than 500 lots suitable for residential development are excluded, alongside suburbs over 30km from the city.